by Andrew Anderson, Vice President of Innovation and Informatics Strategy, (ACD/Labs)
Whether it's laundry detergent, the latest tablet, or a pair of shoes, we’re all familiar with buying products through Amazon. Navigation on the website is easy for consumers, providing a plethora of information on thousands of products in just one click. There’s even a name for it! Marketers refer to this as “The Amazon Effect.” Anyway, today I want to talk about a popular feature used by the e-commerce giant. Amazon has a unique way of sorting its data and matching similar products to one another. We’ve all seen it and can’t deny browsing through these recommendations. It’s that sidebar titled, “customers who viewed this item also viewed…,” or the familiar list of similar items that Amazon predicts may be of interest to you.
This system-driven recommendation is a way of organizing large sets of data and information based on consumers’ browsing history on Amazon’s website and products that share similar features. In laboratory informatics, scientists organize and analyze data in a very similar way. By using software like ACD/Spectrus Platform, scientists—from different laboratories using a variety of instruments—can combine and process their large data sets in a single interface that delivers quality results.
As we know, scientists live and breathe data. It surrounds them while conducting experiments. Rather than flowing in one direction, such as moving data results from an experiment into a hard-copy presentation for R&D stakeholders, data is fluid and can be acquired in the midst of its lifecycle. When extracted, data not only can be used for preparation when making important decisions, but it can also be used to drive lab operation efficiencies by improving a method or experimental process, such as material characterization or spectral comparisons.
From our observations, scientists expect corporate environments to offer powerful functionality, and are exposed to “consumer” big data application functionality; albeit from their non-working life. To ensure that big data is being used effectively in the lab, interoperability is required between software systems and data sets alike. Luckily for scientists, as I mentioned earlier in this post, ACD/Labs’ software makes this possible.
In our efforts to modernize software for today’s externalized world, as big data is shared across labs and instruments, ACD/Labs’ software has an embedded searching feature, or widget, that has the ability to sift through the data. Users have access to a variety of data parameters to search. Most corporate informatics infrastructures allow users to search text, numbers, and chemical structures in a relatively interoperable fashion. However, when it comes to “unabstracted” analytical data (think spectra, chromatograms, etc.), interoperable searching is quite limited. Moreover, spectral and chromatographic “similarity” is important to leverage for identifying related data—similar to the Amazon-esque recommendation functionality that scientists are used to from their experience as everyday consumers.
In today’s digital world, whether you’re shopping online or conducting experiments in the lab, facing big data is all about navigating your way through the extensive amounts of information available to us in an easier way. In other words, finding what we need in one simple search. Learn more about how ACD/Spectrus can help organize and manage your big data here.